Massive 3D point cloud visualization by generating artificial center points from multi-resolution cube grid structure

Seung Chan Yang, Soo Hee Han, Joon Heo

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

Original languageEnglish
Pages (from-to)335-342
Number of pages8
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume30
Issue number4
DOIs
Publication statusPublished - 2012

Fingerprint

visualization
civil engineering
scanner
segmentation
laser
speed

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

Cite this

@article{6ae5513385c546f1be9b2e3f52561848,
title = "Massive 3D point cloud visualization by generating artificial center points from multi-resolution cube grid structure",
abstract = "3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.",
author = "Yang, {Seung Chan} and Han, {Soo Hee} and Joon Heo",
year = "2012",
doi = "10.7848/ksgpc.2012.30.4.335",
language = "English",
volume = "30",
pages = "335--342",
journal = "Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography",
issn = "1598-4850",
publisher = "Korean Society of Surveying, Geodesy, Photogrammetry and Cartography",
number = "4",

}

TY - JOUR

T1 - Massive 3D point cloud visualization by generating artificial center points from multi-resolution cube grid structure

AU - Yang, Seung Chan

AU - Han, Soo Hee

AU - Heo, Joon

PY - 2012

Y1 - 2012

N2 - 3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

AB - 3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

UR - http://www.scopus.com/inward/record.url?scp=84875728926&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875728926&partnerID=8YFLogxK

U2 - 10.7848/ksgpc.2012.30.4.335

DO - 10.7848/ksgpc.2012.30.4.335

M3 - Article

AN - SCOPUS:84875728926

VL - 30

SP - 335

EP - 342

JO - Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography

JF - Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography

SN - 1598-4850

IS - 4

ER -